Value of information (VOI) analyses can help policy makers make informed decisions about whether to conduct and how to design future studies. Historically a computationally expensive method to compute the expected value of sample information (EVSI) restricted the use of VOI to simple decision models and study designs. Recently, 4 EVSI approximation methods have made such analyses more feasible and accessible. Members of the Collaborative Network for Value of Information (ConVOI) compared the inputs, the analyst's expertise and skills, and the software required for the 4 recently developed EVSI approximation methods. Our report provides practical guidance and recommendations to help inform the choice between the 4 efficient EVSI estimation methods. More specifically, this report provides: (1) a step-by-step guide to the methods' use, (2) the expertise and skills required to implement the methods, and (3) method recommendations based on the features of decision-analytic problems.

Enhanced decision support for policy makers using a web interface to health-economic models -Illustrated with a cost-effectiveness analysis of nation-wide infant vaccination with the 7-valent pneumococcal conjugate vaccine in the Netherlands Hubben, G.A.A.; Bos, J.M.; Glynn, D.M.; op 't Ende, Anna; van Alphen, L.; Postma, Maarten Take-down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons the number of authors shown on this cover page is limited to 10 maximum. AbstractWe have developed a web-based user-interface (web interface) to enhance the usefulness of health-economic evaluations to support decision making (http://pcv.healtheconomics.nl). It allows the user to interact with a health-economic model to evaluate predefined and customized scenarios and perform sensitivity analysis. To explore its usefulness, it was applied to an evaluation of cost-effectiveness of nation-wide infant vaccination with the 7-valent pneumococcal conjugate vaccine (PCV7), that was used to support a policy decision on the inclusion of PCV7 in the national vaccination program (NVP) of the Netherlands. We used a decision-tree analytic model to project the impact of infant vaccination with four doses of PCV7 on an annual cohort of infants born in the Netherlands. The base-case analysis includes the beneficial effects on unvaccinated individuals (herd protection). Additional scenarios varying the number of doses, discount rate for effects and the number of serotypes in the vaccine were evaluated and can be analysed on the web. Our model projects a base-case incremental cost-effectiveness ratio (iCER) of D 14,000 (95% uncertainty interval (UI): 9,800-20,200) per quality adjusted life year (QALY) or D 15,600 (95% UI: 11,100-23,900) per life year gained (LYG).

BackgroundIt has been highlighted in both Poland and the United States of America (USA) that knowledge of idiopathic scoliosis (IS) among physiotherapy students is limited with respect to the 2011 International Society on Scoliosis Orthopaedic and Rehabilitation Treatment (SOSORT) guidelines. Early detection of scoliosis and correct initial management is essential in effective care, and thus physiotherapists should be aware of the basic criteria for diagnosis and indications for treatment. The aim of this study was to evaluate the basic knowledge of IS in physiotherapy students trained in the United Kingdom (UK).MethodsA previously designed and tested 10-question survey, including knowledge of the 2011 SOSORT guidelines, was transcribed onto an online-survey platform. Questions were designed to analyse knowledge of definition, cause, development, prevalence, diagnosis, treatment and bracing of scoliosis.All UK universities offering physiotherapy degrees were invited to participate, with the programme lead of each institution asked to distribute the questionnaire to all penultimate and final year physiotherapy students (bachelor’s and master’s degrees). The final number of students who received the study invitation is unknown. The survey link closed after 8 weeks of data collection.Results Two hundred and six students, split over 12 institutions, successfully completed the questionnaire.Analysis showed that 79% of students recognised when IS is likely to develop, yet only 52% recognised that IS’s aetiology is unknown. Eighty-eight percent of students incorrectly defined IS as a 2-dimensional deformity, with only 24% successfully recognising the prevalence of IS within the scoliosis population. Just 12% knew the criteria for diagnosis; however, 93% were unable to recognise the appropriate treatment approach through therapeutic exercise. Finally, 54% of students managed to identify correctly when bracing is recommended for IS.In comparison to previous studies within the USA, students in the UK performed worse in relation to all questions except treatment (7% answered correctly vs 3% in the American study).ConclusionWith only 7% of students able to answer > 50% of the survey questions correctly, there is a clear lack of knowledge of appropriate IS diagnosis and care which could directly impact the information these patients are given within the first contact primary care in the UK.Electronic supplementary materialThe online version of this article (10.1186/s13013-017-0141-z) contains supplementary material, which is available to authorized users.

The expected value of sample information (EVSI) can be used to prioritize avenues for future research and design studies that support medical decision making and offer value for money spent. EVSI is calculated based on 3 key elements. Two of these, a probabilistic model-based economic evaluation and updating model uncertainty based on simulated data, have been frequently discussed in the literature. By contrast, the third element, simulating data from the proposed studies, has received little attention. This tutorial contributes to bridging this gap by providing a step-by-step guide to simulating study data for EVSI calculations. We discuss a general-purpose algorithm for simulating data and demonstrate its use to simulate 3 different outcome types. We then discuss how to induce correlations in the generated data, how to adjust for common issues in study implementation such as missingness and censoring, and how individual patient data from previous studies can be leveraged to undertake EVSI calculations. For all examples, we provide comprehensive code written in the R language and, where possible, Excel spreadsheets in the supplementary materials. This tutorial facilitates practical EVSI calculations and allows EVSI to be used to prioritize research and design studies.

Background The National Institute for Health and Care Excellence and a number of international health technology assessment agencies have recently undertaken appraisals of histology-independent technologies (HITs). A strong and untested assumption inherent in the submissions included identical clinical response across all tumour histologies, including new histologies unrepresented in the trial. Challenging this assumption and exploring the potential for heterogeneity has the potential to impact upon cost-effectiveness. Method Using published response data for a HIT, a Bayesian hierarchical model (BHM) was used to identify heterogeneity in response and to estimate the probability of response for each histology included in single-arm studies, which informed the submission for the HIT, larotrectinib. The probability of response for a new histology was estimated. Results were inputted into a simplified response-based economic model using hypothetical parameters. Histology-independent and histology-specific incremental cost-effectiveness ratios accounting for heterogeneity were generated. Results The results of the BHM show considerable heterogeneity in response rates across histologies. The predicted probability of response estimated by the BHM is 60.9% (95% credible interval 16.0; 91.8%), lower than the naively pooled probability of 74.5%. A mean response probability of 56.9% (0.2; 99.9%) is predicted for an unrepresented histology. Based on the economic analysis, the probability of the hypothetical HIT being cost-effective under the assumption of identical response is 78%. Allowing for heterogeneity, the probability of various approval decisions being cost-effective ranges from 93% to 11%. Conclusions Central to the challenge of reimbursement of HITs is the potential for heterogeneity. This study illustrates how heterogeneity in clinical effectiveness can result in highly variable and uncertain estimates of cost-effectiveness. This analysis can help improve understanding of the consequences of histology-independent versus histology-specific decisions.